




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_	     =       {
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       ['ElectraTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       [
      'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'ElectraForCausalLM',
      'ElectraForMaskedLM',
      'ElectraForMultipleChoice',
      'ElectraForPreTraining',
      'ElectraForQuestionAnswering',
      'ElectraForSequenceClassification',
      'ElectraForTokenClassification',
      'ElectraModel',
      'ElectraPreTrainedModel',
      'load_tf_weights_in_electra',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       [
      'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFElectraForMaskedLM',
      'TFElectraForMultipleChoice',
      'TFElectraForPreTraining',
      'TFElectraForQuestionAnswering',
      'TFElectraForSequenceClassification',
      'TFElectraForTokenClassification',
      'TFElectraModel',
      'TFElectraPreTrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       [
      'FlaxElectraForCausalLM',
      'FlaxElectraForMaskedLM',
      'FlaxElectraForMultipleChoice',
      'FlaxElectraForPreTraining',
      'FlaxElectraForQuestionAnswering',
      'FlaxElectraForSequenceClassification',
      'FlaxElectraForTokenClassification',
      'FlaxElectraModel',
      'FlaxElectraPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
  from .tokenization_electra import ElectraTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_electra_fast import ElectraTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_electra import (
        ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        ElectraForCausalLM,
        ElectraForMaskedLM,
        ElectraForMultipleChoice,
        ElectraForPreTraining,
        ElectraForQuestionAnswering,
        ElectraForSequenceClassification,
        ElectraForTokenClassification,
        ElectraModel,
        ElectraPreTrainedModel,
        load_tf_weights_in_electra,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_electra import (
        TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFElectraForMaskedLM,
        TFElectraForMultipleChoice,
        TFElectraForPreTraining,
        TFElectraForQuestionAnswering,
        TFElectraForSequenceClassification,
        TFElectraForTokenClassification,
        TFElectraModel,
        TFElectraPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_electra import (
        FlaxElectraForCausalLM,
        FlaxElectraForMaskedLM,
        FlaxElectraForMultipleChoice,
        FlaxElectraForPreTraining,
        FlaxElectraForQuestionAnswering,
        FlaxElectraForSequenceClassification,
        FlaxElectraForTokenClassification,
        FlaxElectraModel,
        FlaxElectraPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE_	     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






"""simple docstring"""





import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      lowerCAmelCase    		=			ascii_letters + digits + punctuation
      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      i -= len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			i // 3
      lowerCAmelCase    		=			i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      lowerCAmelCase    		=			(
          chars_incl
          + random(SCREAMING_SNAKE_CASE				,		quotient + remainder     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
          + random(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
      )
      lowerCAmelCase    		=			list(SCREAMING_SNAKE_CASE     )
      shuffle(SCREAMING_SNAKE_CASE     )
      return "".join(SCREAMING_SNAKE_CASE     )

      # random is a generalised function for letters, characters and numbers


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      return "".join(secrets.choice(SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      pass  # Put your code here...


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		int = 8     ):







      '''simple docstring'''




      if len(SCREAMING_SNAKE_CASE     ) < min_length:
            # Your Password must be at least 8 characters long
            return False

      lowerCAmelCase    		=			any(char in ascii_uppercase for char in password     )
      lowerCAmelCase    		=			any(char in ascii_lowercase for char in password     )
      lowerCAmelCase    		=			any(char in digits for char in password     )
      lowerCAmelCase    		=			any(char in punctuation for char in password     )

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			int(input("""Please indicate the max length of your password: """     ).strip()     )
      lowerCAmelCase    		=			input(
          """Please indicate the characters that must be in your password: """     ).strip()
      print("""Password generated:"""				,		password_generator(SCREAMING_SNAKE_CASE     )     )
      print(
          """Alternative Password generated:"""				,		alternative_password_generator(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )				,		)
      print("""[If you are thinking of using this passsword, You better save it.]"""     )


if __name__ == "__main__":
       main()






